Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome
| Autor: | Matthias Mitschke, Shadi Ebrahimian, Mathis Zimmermann, Rosa Babaei, Felix Durlak, Bernardo Bizzo, Varun Buch, Mannudeep K. Kalra, Fatemeh Homayounieh, Marcio Aloisio Bezerra Cavalcanti Rockenbach, Subba R. Digumarthy, Ruhani Doda Khera, Iman Mohseni, Franziska Rauch, Hadi Karimi Mobin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male medicine.medical_specialty medicine.medical_treatment Logistic regression Severity of Illness Index Article 030218 nuclear medicine & medical imaging law.invention 03 medical and health sciences 0302 clinical medicine law medicine Humans Radiology Nuclear Medicine and imaging Lung volumes COVID-19 pneumonia Lung Retrospective Studies Mechanical ventilation Radiological and Ultrasound Technology business.industry SARS-CoV-2 COVID-19 Deep learning medicine.disease Prognosis Intensive care unit Patient outcome Confidence interval Motion artifacts Computer Science Applications Pneumonia medicine.anatomical_structure Observational study Female Radiology business Tomography X-Ray Computed 030217 neurology & neurosurgery CT |
| Zdroj: | Journal of Digital Imaging |
| ISSN: | 1618-727X 0897-1889 |
| Popis: | To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ −200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94–0.97) as well as for predicting patient outcome (AUC 0.7–0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76–0.82, 95% confidence interval (CI) 0.73–0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69–0.77, 95% CI 0.69–0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink